Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures

Molecular Changes in Breast Cancer Induced by Radiation Therapy

PURPOSE

Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT.

METHODS AND MATERIALS

Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand.

RESULTS

The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome.

CONCLUSIONS

Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

Large-scale foundation model on single-cell transcriptomics

Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.

Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer

Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present.In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

AURKAIP1 actuates tumor progression through stabilizing DDX5 in triple negative breast cancer

Aurora-A kinase interacting protein 1 (AURKAIP1) has been proved to take an intermediary role in cancer by functioning as a negative regulator of Aurora-A kinase. However, it remains unclear whether and how AURKAIP1 itself would directly engage in regulating malignancies. The expression levels of AURKAIP1 were detected in triple negative breast cancer (TNBC) by immunohistochemistry and western blots. The CCK8, colony formation assays and nude mouse model were conducted to determine cell proliferation whereas transwell and wound healing assays were performed to observe cell migration. The interaction of AURKAIP1 and DEAD-box helicase 5 (DDX5) were verified through co-immunoprecipitation and successively western blots. From the results, we found that AURKAIP1 was explicitly upregulated in TNBC, which was positively associated with tumor size, lymph node metastases, pathological stage and unfavorable prognosis. AURKAIP1 silencing markedly inhibited TNBC cell proliferation and migration in vitro and in vivo. AURKAIP1 directly interacted with and stabilized DDX5 protein by preventing ubiquitination and degradation, and DDX5 overexpression successfully reversed proliferation inhibition induced by knockdown of AURKAIP1. Consequently, AURKAIP1 silencing suppressed the activity of Wnt/β-catenin signaling in a DDX5-dependent manner. Our study may primarily disclose the molecular mechanism by which AURKAIP1/DDX5/β-catenin axis modulated TNBC progression, indicating that AURKAIP1 might serve as a therapeutic target as well as a TNBC-specific biomarker for prognosis.

Quantification of ligand and mutation-induced bias in EGFR phosphorylation in direct response to ligand binding

Signaling bias is the ability of a receptor to differentially activate downstream signaling pathways in response to different ligands. Bias investigations have been hindered by inconsistent results in different cellular contexts. Here we introduce a methodology to identify and quantify bias in signal transduction across the plasma membrane without contributions from feedback loops and system bias. We apply the methodology to quantify phosphorylation efficiencies and determine absolute bias coefficients. We show that the signaling of epidermal growth factor receptor (EGFR) to EGF and TGFα is biased towards Y1068 and against Y1173 phosphorylation, but has no bias for epiregulin. We further show that the L834R mutation found in non-small-cell lung cancer induces signaling bias as it switches the preferences to Y1173 phosphorylation. The knowledge gained here challenges the current understanding of EGFR signaling in health and disease and opens avenues for the exploration of biased inhibitors as anti-cancer therapies.

Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers

The most prevalent and aggressive type of brain cancer, namely, glioblastoma (GBM), is characterized by intra- and inter-tumor heterogeneity and strong spreading capacity, which makes treatment ineffective. A true therapeutic answer is still in its infancy despite various studies that have made significant progress toward understanding the mechanisms behind GBM recurrence and its resistance. The primary causes of GBM recurrence are attributed to the heterogeneity and diffusive nature; therefore, monitoring the tumor's heterogeneity and spreading may offer a set of therapeutic targets that could improve the clinical management of GBM and prevent tumor relapse. Additionally, the blood-brain barrier (BBB)-related poor drug delivery that prevents effective drug concentrations within the tumor is discussed. With a primary emphasis on signaling heterogeneity, tumor infiltration, and computational modeling of GBM, this review covers typical therapeutic difficulties and factors contributing to drug resistance development and discusses potential therapeutic approaches.

Research progress of cancer cell membrane coated nanoparticles for the diagnosis and therapy of breast cancer

Nanoparticles (NPs) disguised in the cell membrane are a new type of biomimetic platform. Due to their ability to simulate the unique biological functions of membrane-derived cells, they have become one of the hotspots of research at home and abroad. The tumor-specific antigen antibody carried by breast cancer cell membranes can modify nanoparticles to have homologous tumor targeting. Therefore, nanoparticles wrapped in cancer cell membranes have been widely used in research on the diagnosis and treatment of breast cancer. This article reviews the current situation, prospects, advantages and limitations of nanoparticles modified by cancer cell membranes in the treatment and diagnosis of breast cancer.

Opto-microfluidic assisted synthesis of photo-protoporphyrin (pPP) conjugated to hollow gold-albumin hybrid nanoshells to enhance the efficiency of photodynamic therapy of triple negative breast cancer cells

INTRODUCTION

Protoporphyrin-IX (PpIX), a photosensitizer used in photodynamic therapy, has limitations due to its hydrophobicity, rapid photobleaching, and low absorption peak in the red region. These limitations make the use of PpIX less effective for photodynamic therapy treatments. In this study, we harnessed the power of microfluidic technology to manipulate the properties of PpIX and quickly synthesize albumin-based hybrid nanoshells with high reproducibility.

METHODS AND MATERIAL

To begin with, we designed a microfluidic chip with SolidWorksⓇ software; then the chip was fabricated in Poly(methyl methacrylate) (PMMA) material using micromilling and thermal bonding. We synthesized PpIX-loaded CTAB micelles and subsequently transformed the PpIX structure into photo-protoporphyrin (PPP,) by opto-microfluidic chip (Integrating a microfluidic chip with a light source). Simultaneously with CTAB-PPP synthesis complex, we trapped it in binding sites of bovine serum albumin (BSA). Afterward, we used the same method (without irradiating) to generate a hybrid nanostructure consisting of hollow gold nanoshells (HGN) and BSACTAB-PPP. Then, after physical characterization of nanostructures, the photodynamic effects of the agents (HGNs, CTAB-PpIX, BSA-CTABPpIX, HGN-BSA-CTAB-PpIX, CTAB-PPP, BSA-CTAB-PPP, and HGNs-BSA-CTAB-PPP) were evaluated on MDA-MB-231 and 4T1 cells and the cytotoxic properties of the therapeutic agents after treatment for 24, 48, and 72 hours were investigated using MTT assay. Finally, we analyzed the findings using GraphPad Prism 9.0 software.

RESULTS

Results revealed that the opto-microfluidic assisted synthesis of HGN-BSA-CTAB-PPP is highly efficient and reproducible, with a size of 120 nm, a zeta potential of -16 mV, and a PDI index of 0.357. Furthermore, the cell survival analysis demonstrated that the HGNBSA-CTAB-PPP hybrid nanostructure can significantly reduce the survival of MDA-MB-231 and 4T1 cancer cells at low radiation doses (< 10 J/cm2) when exposed to an incoherent light source due to its strong absorption peak at a wavelength of 670 nm.

CONCLUSION

This research indicates that developing albumin-based multidrug hybrid nanostructures using microfluidic technology could be a promising approach to design more efficient photodynamic therapy studies.

PRR15 deficiency facilitates malignant progression by mediating PI3K/Akt signaling and predicts clinical prognosis in triple-negative rather than non-triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast neoplasms with a higher risk of recurrence and metastasis than non-TNBC. Nevertheless, the factors responsible for the differences in the malignant behavior between TNBC and non-TNBC are not fully explored. Proline rich 15 (PRR15) is a protein involved in the progression of several tumor types, but its mechanisms are still controversial. Therefore, this study aimed to investigate the biological role and clinical applications of PRR15 on TNBC. PRR15 gene was differentially expressed between TNBC and non-TNBC patients, previously described as an oncogenic factor in breast cancer. However, our results showed a decreased expression of PRR15 that portended a favorable prognosis in TNBC rather than non-TNBC. PRR15 knockdown facilitated the proliferation, migration, and invasive ability of TNBC cells in vitro and in vivo, which was abolished by PRR15 restoration, without remarkable effects on non-TNBC. High-throughput drug sensitivity revealed that PI3K/Akt signaling was involved in the aggressive properties of PRR15 silencing, which was confirmed by the PI3K/Akt signaling activation in the tumors of PRR15^Low patients, and PI3K inhibitor reversed the metastatic capacity of TNBC in mice. The reduced PRR15 expression in TNBC patients was positively correlated with more aggressive clinicopathological characteristics, enhanced metastasis, and poor disease-free survival. Collectively, PRR15 down-regulation promotes malignant progression through the PI3K/Akt signaling in TNBC rather than in non-TNBC, affects the response of TNBC cells to antitumor agents, and is a promising indicator of disease outcomes in TNBC.

Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD

The single-cell RNA sequencing (scRNA-seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA-seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti-cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA-Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single-cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre-clinical cell lines to infer pre-existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug-resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat-resistant while Gefitinib-sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution.

Targeted Therapy and Mechanisms of Drug Resistance in Breast Cancer

Breast cancer is the most common cause of cancer-related death in women worldwide. Multidrug resistance (MDR) has been a large hurdle in reducing BC death rates. The drug resistance mechanisms include increased drug efflux, enhanced DNA repair, senescence escape, epigenetic alterations, tumor heterogeneity, tumor microenvironment (TME), and the epithelial-to-mesenchymal transition (EMT), which make it challenging to overcome. This review aims to explain the mechanisms of resistance in BC further, identify viable drug targets, and elucidate how those targets relate to the progression of BC and drug resistance.

Model selection for assessing the effects of doxorubicin on triple-negative breast cancer cell lines

Doxorubicin is a chemotherapy widely used to treat several types of cancer, including triple-negative breast cancer. In this work, we use a Bayesian framework to rigorously assess the ability of ten different mathematical models to describe the dynamics of four TNBC cell lines (SUM-149PT, MDA-MB-231, MDA-MB-453, and MDA-MB-468) in response to treatment with doxorubicin at concentrations ranging from 10 to 2500 nM. Each cell line was plated and serially imaged via fluorescence microscopy for 30 days following 6, 12, or 24 h of in vitro drug exposure. We use the resulting data sets to estimate the parameters of the ten pharmacodynamic models using a Bayesian approach, which accounts for uncertainties in the models, parameters, and observational data. The ten candidate models describe the growth patterns and degree of response to doxorubicin for each cell line by incorporating exponential or logistic tumor growth, and distinct forms of cell death. Cell line and treatment specific model parameters are then estimated from the experimental data for each model. We analyze all competing models using the Bayesian Information Criterion (BIC), and the selection of the best model is made according to the model probabilities (BIC weights). We show that the best model among the candidate set of models depends on the TNBC cell line and the treatment scenario, though, in most cases, there is great uncertainty in choosing the best model. However, we show that the probability of being the best model can be increased by combining treatment data with the same total drug exposure. Our analysis points to the importance of considering multiple models, built on different biological assumptions, to capture the observed variations in tumor growth and treatment response.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

BACKGROUND

Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.

METHODS

In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.

RESULTS

Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).

CONCLUSIONS

We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Identification of a novel signature with prognostic value in triple-negative breast cancer through clinico-transcriptomic analysis

Background

Although perceived as a highly aggressive disease, triple-negative breast cancer (TNBC) constitutes heterogeneous features with various outcomes. In this study, we aimed to establish a prognostic signature for patients with TNBC to improve risk stratification.

Methods

Gene expression data were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were detected pairwise between TNBC and other subtypes of samples. Then, TNBC-correlated modules were determined using coexpression network analysis. A gene signature was established based on the prognostic genes in the intersection between DEGs and selected gene modules using least absolute shrinkage and selection operator (LASSO) Cox regression. Finally, a clinico-transcriptomic signature was developed to predict overall survival (OS). Model performance was quantified, and the bootstrap resampling method was used for validation.

Results

The gene signature included 6 messenger RNAs (mRNAs) and a clinical score indicating an increased likelihood of death when used as continuous or categorical predictors. A nomogram was built by integrating the pathological stage and gene signature to predict 2-, 3-, and 5-year OS. The addition of pathological stage increased the concordance index (C-index) compared with pathological stage alone and the gene signature alone. Bootstrap resampling revealed a stable performance of the nomogram.

Conclusions

A 6-mRNA signature was established to inform prognosis for patients with TNBC. Its combination with pathological stage can contribute to improving performance and provide additional supporting evidence for clinical decision-making.

Intelligent Biomimetic Nanoplatform for Systemic Treatment of Metastatic Triple-Negative Breast Cancer via Enhanced EGFR-Targeted Therapy and Immunotherapy

Triple-negative breast cancer (TNBC) is the most malignant subtype of breast cancer, and it is associated with a high recurrence rate, metastatic potential, and poor prognosis. Thus, effective therapeutic strategies for TNBC are urgently required. The epidermal growth factor receptor (EGFR) is considered to be a potential therapeutic target for TNBC. However, there are limitations to the use of targeted therapies, such as afatinib (AFT), particularly drug resistance. Here, we investigated a poly(d,l-lactide-glycolide) (PLGA)-based intelligent bionic nanoplatform, termed AFT/2-BP@PLGA@MD, which combined targeted therapy with immunotherapy. In this platform, PLGA was used to encapsulate 2-bromo-palmitate (2-BP), a palmitoylation inhibitor, to enhance the efficacy of AFT against TNBC cells. PLGA was coated with a cancer cell membrane anchored with a cleavable peptide by matrix metalloproteinase-2 to block programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1). 2-BP significantly enhanced the capacity of AFT to inhibit the proliferation and migration of tumor cells in vitro. Moreover, the tumor cell membrane-coated AFT/2-BP@PLGA@MD nanoparticles exhibited enhanced tumor targeting ability in vivo. The AFT/2-BP@PLGA@MD nanoparticles significantly inhibited the growth and metastasis of 4T1 tumor and prolonged the survival of tumor-bearing mice. The nanoparticles also triggered antitumor immune response. Collectively, we report an effective therapeutic strategy for clinically refractory TNBC.